Joint estimation of causal effects from expression data

HIGHLIGHTS

• who: Maximum likelihood and collaborators from the (UNIVERSITY) have published the paper: Joint estimation of causal effects from expression data, in the Journal: (JOURNAL)
• what: The authors seek to improve the estimation of causal effects among genes by jointly modeling observational transcriptomic data with arbitrarily complex intervention data obtained by performing partial single or multiple gene knock-outs or knock-downs. Using the framework of causal Gaussian Bayesian networks the authors propose a Markov chain Monte Carlo algorithm with a Mallows proposal model and analytical maximization to sample from the posterior distribution of causal node . . .
  

   

  

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